# Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation

arXiv: https://arxiv.org/abs/1708.01928 · HTML: https://mlsanity.com/paper/1708.01928/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2017.

## Built on by (most cited first, 17 of 17)

- [Translating Clinical Delineation of Diabetic Foot Ulcers into Machine Interpretable Segmentation](https://mlsanity.com/paper/2204.11618/) (2022)
- [FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation](https://mlsanity.com/paper/2305.02961/) (2023)
- [Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation](https://mlsanity.com/paper/2111.01590/) (2021)
- [Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks](https://mlsanity.com/paper/2109.01408/) (2021)
- [Diabetic Foot Ulcer Grand Challenge 2021: Evaluation and Summary](https://mlsanity.com/paper/2111.10376/) (2021)
- [A Cloud-based Deep Learning Framework for Remote Detection of Diabetic Foot Ulcers](https://mlsanity.com/paper/2105.07763/) (2021)
- [Development of Diabetic Foot Ulcer Datasets: An Overview](https://mlsanity.com/paper/2201.00163/) (2022)
- [CO2Wounds-V2: Extended Chronic Wounds Dataset From Leprosy Patients](https://mlsanity.com/paper/2408.10827/) (2024)
- [An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging](https://mlsanity.com/paper/2410.03359/) (2024)
- [WoundAmbit: Bridging State-of-the-Art Semantic Segmentation and Real-World Wound Care](https://mlsanity.com/paper/2504.06185/) (2025)
- [Quantifying the Effect of Image Similarity on Diabetic Foot Ulcer Classification](https://mlsanity.com/paper/2304.12987/) (2023)
- [Early Explorations of Lightweight Models for Wound Segmentation on Mobile Devices](https://mlsanity.com/paper/2407.07605/) (2024)
- [A Pressure Ulcer Care System For Remote Medical Assistance: Residual U-Net with an Attention Model Based for Wound Area Segmentation](https://mlsanity.com/paper/2101.09433/) (2021)
- [Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers](https://mlsanity.com/paper/2203.03814/) (2022)
- [Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques](https://mlsanity.com/paper/2209.09880/) (2022)
- [Diabetic Foot Ulcer Grand Challenge 2022 Summary](https://mlsanity.com/paper/2304.12001/) (2023)
- [WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring](https://mlsanity.com/paper/2512.18528/) (2025)

## Abstract (arXiv)

Diabetic Foot Ulcer (DFU) is a major complication of Diabetes, which if not managed properly can lead to amputation. DFU can appear anywhere on the foot and can vary in size, colour, and contrast depending on various pathologies. Current clinical approaches to DFU treatment rely on patients and clinician vigilance, which has significant limitations such as the high cost involved in the diagnosis, treatment and lengthy care of the DFU. We introduce a dataset of 705 foot images. We provide the ground truth of ulcer region and the surrounding skin that is an important indicator for clinicians to assess the progress of ulcer. Then, we propose a two-tier transfer learning from bigger datasets to train the Fully Convolutional Networks (FCNs) to automatically segment the ulcer and surrounding skin. Using 5-fold cross-validation, the proposed two-tier transfer learning FCN Models achieve a Dice Similarity Coefficient of 0.794 (±0.104) for ulcer region, 0.851 (±0.148) for surrounding skin region, and 0.899 (±0.072) for the combination of both regions. This demonstrates the potential of FCNs in DFU segmentation, which can be further improved with a larger dataset.